1. Field
The present disclosure relates generally to an improved object detection and tracking system, and in particular, to a method and apparatus for detecting and tracking objects using constrained velocity matched filters.
2. Background
Detecting and tracking objects in real world situations can be a challenging problem. For example, the sensor system may be located far enough away from an object such that the object is dim, making detecting the object more difficult. In particular, the object brightness may be below a desired threshold for the sensor system to detect the presence of the object. In this situation, a lower detection threshold may be used. This lower threshold, however, may result in a higher than desired rate of false alarms.
Additionally, the size of the object may be small enough such that the number of pixels detected for the object may increase the difficulty in identifying characteristics about the object. For example, identifying shape and texture information about the object may be more difficult with the number of pixels available.
With an inability to obtain characteristics about the object, filtering out false detections may be more difficult than desired. With this situation, automatically detecting objects in real-time that are barely visible to the human eye and with the object signal closer to the noise floor may make detecting objects more difficult than desired.
Velocity matched filters (VMFs) have been used to detect and track objects that have a low signal-to-noise ratio (SNR). These types of filters may be implemented in track before detect (TBD) systems.
A velocity matched filter applies a constraint that assumes an object will have a constant velocity over the integration period of the velocity matched filter. These types of filters may be applied to multiple images over time and detect objects having a signal-to-noise ratio that are otherwise undetectable with other detection techniques. When the velocity of the object is unknown, multiple velocity matched filters for different velocities are applied to identify the velocity of the object.
Although the use of velocity matched filters may be useful in tracking objects that have low signal-to-noise ratios, challenges are still present for using velocity matched filters in these systems. These challenges often occur when real-time surveillance is desired.
For example, the systems assume that the number of objects is known ahead of time. In real-time surveillance of an area, this type of information is often unavailable.
In some cases, this problem is solved by including a detection process before the track before detect process is used. However, using this additional detection process may be more challenging than desired when performing real-time surveillance of an area in which difficult to detect objects may be present.
Therefore, it would be desirable to have a method and apparatus that take into account at least some of the issues discussed above, as well as other possible issues. For example, it would be desirable to solve a technical problem with detecting objects in real-time with currently used detection processes that employ velocity matched filters.